Radiolabeled Gold Nanoparticles for the Treatment of Glioblastoma by Convection Enhanced Delivery
Bibliographic record
Abstract
Glioblastoma (GBM) remains one of the most difficult to treat cancers due to the failure to eliminate residual disease. A metal chelating polymer (MCP) was radiolabeled with the β-particle emitting radionuclide 177Lu and conjugated to gold nanoparticles (AuNP) forming a radiation nanomedicine ([177Lu]Lu-MCP-AuNP). In an orthotopic human xenograft model of GBM (U251-Luc), [177Lu]Lu-MCP-AuNP were infused directly to the tumour using convection enhanced delivery (CED). [177Lu]Lu-MCP-AuNP were retained largely in the tumour (225.2 ± 92.3 %ID/g, 14 d p.i.) and radiation absorbed doses were estimated to deliver 599±311 Gy to the tumour with <0.1 Gy absorbed in all other normal organs. Treatments did not induce normal-tissue toxicity. Tumour bioluminescent signal was eliminated by 21 d p.i. and tumours could not be identified by magnetic resonance imaging (MRI) 28 d p.i. after CED of with [177Lu]Lu-MCP-AuNP (1.1±0.2 MBq, 4×1011 particles). Survival was significantly increased compared to unlabeled MCP-AuNP with 62.5% of mice reaching 150 d post-treatment. In an orthotopic syngeneic model of glioma (GL261), the combination of [177Lu]Lu-MCP-AuNP (0.8±0.09 MBq, 4×1011 particles) and anti-PD1 antibodies (3×200 μg), improved survival compared to treatment with [177Lu]Lu-MCP-AuNP alone. Higher doses of [177Lu]Lu-MCP-AuNP (2.7±0.43 MBq, 4×1011 particles) with anti-PD1 antibodies further improved survival up to 218 d post-tumour inoculation. Object location task (OLT) and novel object recognition task (NORT) behavioural tests revealed that CED of [177Lu]Lu-MCP-AuNP with anti-PD1 antibodies did not impact cognitive function or memory. These results suggest that CED of [177Lu]Lu-MCP-AuNP are a promising therapeutic intervention for the treatment of GBM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".